DOCSLIB.ORG

Orthogonal Array Application for Optimized Software Testing

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Orthogonal Array Application for Optimized Software Testing WSEAS TRANSACTIONS on COMPUTERS Ljubomir Lazic and Nikos Mastorakis Orthogonal Array application for optimal combination of software defect detection techniques choices LJUBOMIR LAZICa, NIKOS MASTORAKISb aTechnical Faculty, University of Novi Pazar Vuka Karadžića bb, 36300 Novi Pazar, SERBIA [email protected] http://www.np.ac.yu bMilitary Institutions of University Education, Hellenic Naval Academy Terma Hatzikyriakou, 18539, Piraeu, Greece [email protected] Abstract: - In this paper, we consider a problem that arises in black box testing: generating small test suites (i.e., sets of test cases) where the combinations that have to be covered are specified by input-output parameter relationships of a software system. That is, we only consider combinations of input parameters that affect an output parameter, and we do not assume that the input parameters have the same number of values. To solve this problem, we propose interaction testing, particularly an Orthogonal Array Testing Strategy (OATS) as a systematic, statistical way of testing pair-wise interactions. In software testing process (STP), it provides a natural mechanism for testing systems to be deployed on a variety of hardware and software configurations. The combinatorial approach to software testing uses models to generate a minimal number of test inputs so that selected combinations of input values are covered. The most common coverage criteria are two-way or pairwise coverage of value combinations, though for higher confidence three-way or higher coverage may be required. This paper presents some examples of software-system test requirements and corresponding models for applying the combinatorial approach to those test requirements. The method bridges contributions from mathematics, design of experiments, software test, and algorithms for application to usability testing. Also, this study presents a brief overview of the response surface methods (RSM) for computer experiments available in the literature. The Bayesian approach and orthogonal arrays constructed for computer experiments (OACE) were briefly discussed. An example, of a novel OACE application, to STP optimization study was also given. In this case study, an orthogonal array for computer experiments was utilized to build a second order response surface model. Gradient-based optimization algorithms could not be utilized in this case study since the design variables were discrete valued. Using OACE novel approach, optimum combination of software defect detection techniques choices for every software development phase that maximize all over Defect Detection Effectiveness of STP were determined. Key-Words: - Software testing, Opzimization, Design of Experiments, Orthogonal array 1 Introduction practices that many IT organizations currently have in Many IT organizations struggle with how to place? Simply put, optimized testing is a practical determine the proper balance of testing in light of approach to improving application quality that business demands and budgetary limitations. Testing balances quality, cost and schedules to prioritize has historically been difficult to optimize because of testing and optimize limited resources. The efficiency a number of factors. IT has always faced the of this practical testing approach ensures that the constraints of balancing time, quality and cost. finite testing resources at IT management’s disposal Combine this with the reality that it is often difficult are used to their most productive levels while to measure exactly how effective testing is for a given eliminating harmful defects and errors during the application, and you can easily see that many formal testing process. Optimized testing practices, methods testing processes are far from optimal. Yet many IT and tools now exist that allow the QA team to better organizations are not sure how to make them better. align testing activities with business requirements by So what exactly is optimized testing, and how does it prioritizing testing activities based on two key differ from the quality control and quality assurance factors: importance to the business and risk to the ISSN: 1109-2750 1319 Issue 8, Volume 7, August 2008 WSEAS TRANSACTIONS on COMPUTERS Ljubomir Lazic and Nikos Mastorakis business [25,26]. Business users, development, QA in applying this promising technique in software and other key constituents can now collaborate to testing is the construction of covering designs. There prioritize requirements and identify and prioritize are two issues that need to be considered: the first and project risks. And, when required, they can perhaps more important one is the size of the collaborate to resolve problems during the test covering design since it dictates the number of test execution. Once priorities have been established, QA cases, and consequently, the amount of resources can confidently select the optimum set of tests to needed to test a software system; the second is the thoroughly test the high-priority areas of an time and space requirements of the construction application and adequately test all other areas [25,26]. itself. This paper presents a novel OACE approach for A great deal of research work has been devoted software testing process (STP) optimization study to generating small test suites [2, 3, 7, 12-16, 25]. finding optimum combination of software defect Most researchers focus on uniform coverage of input detection techniques (DDT) choices for every parameters with uniform ranges; i.e., they consider software development phase that maximize all over test suites that cover all t -wise combinations of the Defect Removal Effectiveness (DRE) of STP. The input parameters for some integer t and the input optimum combination of software defect detection parameters are assumed to have the same number of techniques choices were determined applying values. While such test suites apply to a large number orthogonal arrays constructed for post mortem of situations, in practice not all t -wise combinations designed experiment with collected defect data of a of input parameters have equal priority in testing [3, real project [27]. By integrating this unique approach 25], nor are all parameter domains of the same size. to requirements management with test management Testers often prioritize combinations of input and automation tools, tests and test suites can be parameters that influence a system’s output automatically created and executed as in our parameters over those that do not [3, 15, 20]. Integrated and Optimized Software Testing Process Determining which set of input parameters influence (IOSTP) [25,27]. a system’s output parameters can be accomplished Software testing remains an important topic in using existing analyses [3, 25] or can be discovered software engineering. Testing efficiency and in the process of determining the expected result of a effectiveness, two goals which are not always in test case. alignment, are both significantly improved. A recent Covering all pairs of tested factor levels has been report generated for the National Institute of extensively studied. Mandl described using Standards and Technology (NIST) found that orthogonal arrays in testing of a compiler [16]. software defects cost the U.S. economy 59.9 billion Tatsumi, in his paper on Test Case Design Support dollars annually [28]. While current technologies System used in Fujitsu Ltd [22], talks about two cannot hope to remove all errors from software, the standards for creating test arrays: (1) with all report goes on to estimate that 22 billion dollars combinations covered exactly the same number of could be saved through earlier and more effective times (orthogonal arrays) and (2) with all defect detection. combinations covered at least once. When making Black-box testing is a type of software testing that crucial distinction, he references an earlier paper that ensures a program meets its specification from a by Shimokawa and Satoh [19]. Over the years, behavioral or functional perspective. The number of pairwise testing was shown to be an efficient and possible black-box test cases for any non-trivial effective strategy of choosing tests [4-6, 10, 13, 25]. software application is extremely large. The However, as shown by Smith et al. [20] and later by challenge in testing is to reduce the number of test Bach and Shroeder [3] pairwise, like any technique, cases to a subset that can be executed with available needs to be used appropriately and with caution. resources and can also exercise the software Since the probl em of finding a minimal array adequately so that majority of software defects are covering all pairwise combinations of a given set of exposed. One popular method for performing black- test factors is NP-complete [14], understandably a box testing is the use of combinatorial covering considerable amount of research has gone into designs [3-11] based on techniques developed in efficient creation of such arrays. Several strategies designing experiments. These designs correspond to were proposed in an attempt to minimize number of test suites (i.e., a set of test cases) that cover, or tests produced [11]. Authors of these combinatorial execute, combinations of input parameters in a test case generation strategies often describe systematic and effective way, and are most applicable additional considerations that must be taken into in testing data-driven systems where the manipulation account before their solutions become practical. In of data inputs and the relationship between input many cases, they
Load more

Recommended publications
  • Types of Software Testing
    Types of Software Testing We would be glad to have feedback from you. Drop us a line, whether it is a comment, a question, a work proposition or just a hello. You can use either the form below or the contact details on the rightt. Contact details [email protected] +91 811 386 5000 1 Software testing is the way of assessing a software product to distinguish contrasts between given information and expected result. Additionally, to evaluate the characteristic of a product. The testing process evaluates the quality of the software. You know what testing does. No need to explain further. But, are you aware of types of testing. It’s indeed a sea. But before we get to the types, let’s have a look at the standards that needs to be maintained. Standards of Testing The entire test should meet the user prerequisites. Exhaustive testing isn’t conceivable. As we require the ideal quantity of testing in view of the risk evaluation of the application. The entire test to be directed ought to be arranged before executing it. It follows 80/20 rule which expresses that 80% of defects originates from 20% of program parts. Start testing with little parts and extend it to broad components. Software testers know about the different sorts of Software Testing. In this article, we have incorporated majorly all types of software testing which testers, developers, and QA reams more often use in their everyday testing life. Let’s understand them!!! Black box Testing The black box testing is a category of strategy that disregards the interior component of the framework and spotlights on the output created against any input and performance of the system.
    [Show full text]
  • Human Performance Regression Testing
    Human Performance Regression Testing Amanda Swearngin, Myra B. Cohen Bonnie E. John, Rachel K. E. Bellamy Dept. of Computer Science & Eng. IBM T. J. Watson Research Center University of Nebraska-Lincoln, USA P.O. Box 704 Lincoln, NE 68588-0115 Yorktown Heights, NY 10598, USA faswearn,[email protected] fbejohn,[email protected] Abstract—As software systems evolve, new interface features common tasks, necessitating longer mouse movements and, in such as keyboard shortcuts and toolbars are introduced. While fact, decrease efficiency. In addition, many toolbars with small it is common to regression test the new features for functional icons may add screen clutter and may decrease a new user’s correctness, there has been less focus on systematic regression testing for usability, due to the effort and time involved in human ability to discover how to accomplish a task over a simpler studies. Cognitive modeling tools such as CogTool provide some UI design. help by computing predictions of user performance, but they still Usability testing has traditionally been empirical, bringing require manual effort to describe the user interface and tasks, end-users in to a testing facility, asking them to perform tasks limiting regression testing efforts. In recent work, we developed on the system (or prototype), and measuring such things as the CogTool-Helper to reduce the effort required to generate human performance models of existing systems. We build on this work time taken to perform the task, the percentage of end-users by providing task specific test case generation and present our who can complete the task in a fixed amount of time, and vision for human performance regression testing (HPRT) that the number and type of errors made by the end-users.
    [Show full text]
  • Orthogonal Arrays and Row-Column and Block Designs for CDC Systems
    Current Trends on Biostatistics & L UPINE PUBLISHERS Biometrics Open Access DOI: 10.32474/CTBB.2018.01.000103 ISSN: 2644-1381 Review Article Orthogonal Arrays and Row-Column and Block Designs for CDC Systems Mahndra Kumar Sharma* and Mekonnen Tadesse Department of Statistics, Addis Ababa University, Addis Ababa, Ethiopia Received: September 06, 2018; Published: September 20, 2018 *Corresponding author: Mahndra Kumar Sharma, Department of Statistics, Addis Ababa University, Addis Ababa, Ethiopia Abstract In this article, block and row-column designs for genetic crosses such as Complete diallel cross system using orthogonal arrays (p2, r, p, 2), where p is prime or a power of prime and semi balanced arrays (p(p-1)/2, p, p, 2), where p is a prime or power of an odd columnprime, are designs derived. for Themethod block A designsand C are and new row-column and consume designs minimum for Griffing’s experimental methods units. A and According B are found to Guptato be A-optimalblock designs and thefor block designs for Griffing’s methods C and D are found to be universally optimal in the sense of Kiefer. The derived block and row- Keywords:Griffing’s methods Orthogonal A,B,C Array;and D areSemi-balanced orthogonally Array; blocked Complete designs. diallel AMS Cross; classification: Row-Column 62K05. Design; Optimality Introduction d) one set of F ’s hybrid but neither parents nor reciprocals Orthogonal arrays of strength d were introduced and applied 1 F ’s hybrid is included (v = 1/2p(p-1). The problem of generating in the construction of confounded symmetrical and asymmetrical 1 optimal mating designs for CDC method D has been investigated factorial designs, multifactorial designs (fractional replication) by several authors Singh, Gupta, and Parsad [10].
    [Show full text]
  • Evaluation Approaches in Software Testing Ayaz Farooq, Reiner R
    Nr.: FIN-05-2008 Evaluation Approaches in Software Testing Ayaz Farooq, Reiner R. Dumke Arbeitsgruppe Softwaretechnik Fakultät für Informatik Otto-von-Guericke-Universität Magdeburg Impressum (§ 10 MDStV): Herausgeber: Otto-von-Guericke-Universität Magdeburg Fakultät für Informatik Der Dekan Verantwortlich für diese Ausgabe: Otto-von-Guericke-Universität Magdeburg Fakultät für Informatik Reiner Dumke Postfach 4120 39016 Magdeburg E-Mail: [email protected] http://www.cs.uni-magdeburg.de/Preprints.html Auflage: 81 Redaktionsschluss: Juli 2008 Herstellung: Dezernat Allgemeine Angelegenheiten, Sachgebiet Reproduktion Bezug: Universitätsbibliothek/Hochschulschriften- und Tauschstelle Otto-von-Guericke-University of Magdeburg Faculty of Computer Science Institute for Distributed Systems Software Engineering Group Evaluation Approaches in Software Testing Authors: Ayaz Farooq Reiner R. Dumke University of Magdeburg Faculty of Computer Science P.O. Box 4120, 39016 Magdeburg Germany Farooq, Ayaz Dumke, Reiner R. Evaluation Approaches in Software Testing Technical Report Otto-von-Guericke-University of Magdeburg 2008. Contents i Contents 1 Introduction 1 1.1 Evaluation Defined . 2 1.2 Evaluation in Software Engineering . 2 1.3 Evaluation in Software Testing . 3 1.4 Structure of the Report . 5 2 Test Processes: Basics & Maturities 7 2.1 Test Process Fundamentals . 7 2.1.1 Test Process Contexts . 9 2.1.2 Research over Test Process . 10 2.2 Test Process Definition & Modeling . 11 2.2.1 Generic Test Process Descriptions . 11 2.2.1.1 Test Management Approach-TMap . 13 2.2.1.2 Drabick’s Formal Testing Process . 13 2.2.1.3 Test Driven Development . 15 2.2.1.4 Independent Verification & Validation . 17 2.2.2 Domain Specific Test Processes .
    [Show full text]
  • Orthogonal Array Experiments and Response Surface Methodology
    Unit 7: Orthogonal Array Experiments and Response Surface Methodology • A modern system of experimental design. • Orthogonal arrays (sections 8.1-8.2; appendix 8A and 8C). • Analysis of experiments with complex aliasing (part of sections 9.1-9.4). • Brief response surface methodology, central composite designs (sections 10.1-10.2). 1 Two Types of Fractional Factorial Designs • Regular (2n−k, 3n−k designs): columns of the design matrix form a group over a finite field; the interaction between any two columns is among the columns, ⇒ any two factorial effects are either orthogonal or fully aliased. • Nonregular (mixed-level designs, orthogonal arrays) some pairs of factorial effects can be partially aliased ⇒ more complex aliasing pattern. This includes 3n−k designs with linear-quadratic system. 2 A Modern System of Experimental Design It has four branches: • Regular orthogonal arrays (Fisher, Yates, Finney, ...): 2n−k, 3n−k designs, using minimum aberration criterion. • Nonregular orthogonal designs (Plackett-Burman, Rao, Bose): Plackett-Burman designs, orthogonal arrays. • Response surface designs (Box): fitting a parametric response surface. • Optimal designs (Kiefer): optimality driven by specific model/criterion. 3 Orthogonal Arrays • In Tables 1 and 2, the design used does not belong to the 2k−p series (Chapter 5) or the 3k−p series (Chapter 6), because the latter would require run size as a power of 2 or 3. These designs belong to the class of orthogonal arrays. m1 mγ • An orthogonal array array OA(N,s1 ...sγ ,t) of strength t is an N × m matrix, m = m1 + ... + mγ, in which mi columns have si(≥ 2) symbols or levels such that, for any t columns, all possible combinations of symbols appear equally often in the matrix.
    [Show full text]
  • Software Testing: Essential Phase of SDLC and a Comparative Study Of
    International Journal of System and Software Engineering Volume 5 Issue 2, December 2017 ISSN.: 2321-6107 Software Testing: Essential Phase of SDLC and a Comparative Study of Software Testing Techniques Sushma Malik Assistant Professor, Institute of Innovation in Technology and Management, Janak Puri, New Delhi, India. Email: [email protected] Abstract: Software Development Life-Cycle (SDLC) follows In the software development process, the problem (Software) the different activities that are used in the development of a can be dividing in the following activities [3]: software product. SDLC is also called the software process ∑ Understanding the problem and it is the lifeline of any Software Development Model. ∑ Decide a plan for the solution Software Processes decide the survival of a particular software development model in the market as well as in ∑ Coding for the designed solution software organization and Software testing is a process of ∑ Testing the definite program finding software bugs while executing a program so that we get the zero defect software. The main objective of software These activities may be very complex for large systems. So, testing is to evaluating the competence and usability of a each of the activity has to be broken into smaller sub-activities software. Software testing is an important part of the SDLC or steps. These steps are then handled effectively to produce a because through software testing getting the quality of the software project or system. The basic steps involved in software software. Lots of advancements have been done through project development are: various verification techniques, but still we need software to 1) Requirement Analysis and Specification: The goal of be fully tested before handed to the customer.
    [Show full text]
  • Latin Squares in Experimental Design
    Latin Squares in Experimental Design Lei Gao Michigan State University December 10, 2005 Abstract: For the past three decades, Latin Squares techniques have been widely used in many statistical applications. Much effort has been devoted to Latin Square Design. In this paper, I introduce the mathematical properties of Latin squares and the application of Latin squares in experimental design. Some examples and SAS codes are provided that illustrates these methods. Work done in partial fulfillment of the requirements of Michigan State University MTH 880 advised by Professor J. Hall. 1 Index Index ............................................................................................................................... 2 1. Introduction................................................................................................................. 3 1.1 Latin square........................................................................................................... 3 1.2 Orthogonal array representation ........................................................................... 3 1.3 Equivalence classes of Latin squares.................................................................... 3 2. Latin Square Design.................................................................................................... 4 2.1 Latin square design ............................................................................................... 4 2.2 Pros and cons of Latin square design...................................................................
    [Show full text]
  • 1. Can You Explain the PDCA Cycle and Where Testing Fits In?
    1. Can you explain the PDCA cycle and where testing fits in? Software testing is an important part of the software development process. In normal software development there are four important steps, also referred to, in short, as the PDCA (Plan, Do, Check, Act) cycle. Let's review the four steps in detail. 1. Plan: Define the goal and the plan for achieving that goal. 2. Do/Execute: Depending on the plan strategy decided during the plan stage we do execution accordingly in this phase. 3. Check: Check/Test to ensure that we are moving according to plan and are getting the desired results. 4. Act: During the check cycle, if any issues are there, then we take appropriate action accordingly and revise our plan again. So developers and other stakeholders of the project do the "planning and building," while testers do the check part of the cycle. Therefore, software testing is done in check part of the PDCA cyle. 2. What is the difference between white box, black box, and gray box testing? Black box testing is a testing strategy based solely on requirements and specifications. Black box testing requires no knowledge of internal paths, structures, or implementation of the software being tested. White box testing is a testing strategy based on internal paths, code structures, and implementation of the software being tested. White box testing generally requires detailed programming skills. There is one more type of testing called gray box testing. In this we look into the "box" being tested just long enough to understand how it has been implemented.
    [Show full text]
  • Generating and Improving Orthogonal Designs by Using Mixed Integer Programming
    European Journal of Operational Research 215 (2011) 629–638 Contents lists available at ScienceDirect European Journal of Operational Research journal homepage: www.elsevier.com/locate/ejor Stochastics and Statistics Generating and improving orthogonal designs by using mixed integer programming a, b a a Hélcio Vieira Jr. ⇑, Susan Sanchez , Karl Heinz Kienitz , Mischel Carmen Neyra Belderrain a Technological Institute of Aeronautics, Praça Marechal Eduardo Gomes, 50, 12228-900, São José dos Campos, Brazil b Naval Postgraduate School, 1 University Circle, Monterey, CA, USA article info abstract Article history: Analysts faced with conducting experiments involving quantitative factors have a variety of potential Received 22 November 2010 designs in their portfolio. However, in many experimental settings involving discrete-valued factors (par- Accepted 4 July 2011 ticularly if the factors do not all have the same number of levels), none of these designs are suitable. Available online 13 July 2011 In this paper, we present a mixed integer programming (MIP) method that is suitable for constructing orthogonal designs, or improving existing orthogonal arrays, for experiments involving quantitative fac- Keywords: tors with limited numbers of levels of interest. Our formulation makes use of a novel linearization of the Orthogonal design creation correlation calculation. Design of experiments The orthogonal designs we construct do not satisfy the definition of an orthogonal array, so we do not Statistics advocate their use for qualitative factors. However, they do allow analysts to study, without sacrificing balance or orthogonality, a greater number of quantitative factors than it is possible to do with orthog- onal arrays which have the same number of runs.
    [Show full text]
  • Orthogonal Array Sampling for Monte Carlo Rendering
    Eurographics Symposium on Rendering 2019 Volume 38 (2019), Number 4 T. Boubekeur and P. Sen (Guest Editors) Orthogonal Array Sampling for Monte Carlo Rendering Wojciech Jarosz1 Afnan Enayet1 Andrew Kensler2 Charlie Kilpatrick2 Per Christensen2 1Dartmouth College 2Pixar Animation Studios (a) Jittered 2D projections (b) Multi-Jittered 2D projections (c) (Correlated) Multi-Jittered 2D projections wy wu wv wy wu wv wy wu wv y wx wx wx u wy wy wy v wu wu wu x y u x y u x y u Figure 1: We create high-dimensional samples which simultaneously stratify all bivariate projections, here shown for a set of 25 4D samples, along with their six stratified 2D projections and expected power spectra. We can achieve 2D jittered stratifications (a), optionally with stratified 1D (multi-jittered) projections (b). We can further improve stratification using correlated multi-jittered (c) offsets for primary dimension pairs (xy and uv) while maintaining multi-jittered properties for cross dimension pairs (xu, xv, yu, yv). In contrast to random padding, which degrades to white noise or Latin hypercube sampling in cross dimensional projections (cf. Fig.2), we maintain high-quality stratification and spectral properties in all 2D projections. Abstract We generalize N-rooks, jittered, and (correlated) multi-jittered sampling to higher dimensions by importing and improving upon a class of techniques called orthogonal arrays from the statistics literature. Renderers typically combine or “pad” a collection of lower-dimensional (e.g. 2D and 1D) stratified patterns to form higher-dimensional samples for integration. This maintains stratification in the original dimension pairs, but looses it for all other dimension pairs.
    [Show full text]
  • Association Schemes for Ordered Orthogonal Arrays and (T, M, S)-Nets W
    Canad. J. Math. Vol. 51 (2), 1999 pp. 326–346 Association Schemes for Ordered Orthogonal Arrays and (T, M, S)-Nets W. J. Martin and D. R. Stinson Abstract. In an earlier paper [10], we studied a generalized Rao bound for ordered orthogonal arrays and (T, M, S)-nets. In this paper, we extend this to a coding-theoretic approach to ordered orthogonal arrays. Using a certain association scheme, we prove a MacWilliams-type theorem for linear ordered orthogonal arrays and linear ordered codes as well as a linear programming bound for the general case. We include some tables which compare this bound against two previously known bounds for ordered orthogonal arrays. Finally we show that, for even strength, the LP bound is always at least as strong as the generalized Rao bound. 1 Association Schemes In 1967, Sobol’ introduced an important family of low discrepancy point sets in the unit cube [0, 1)S. These are useful for quasi-Monte Carlo methods such as numerical inte- gration. In 1987, Niederreiter [13] significantly generalized this concept by introducing (T, M, S)-nets, which have received considerable attention in recent literature (see [3] for a survey). In [7], Lawrence gave a combinatorial characterization of (T, M, S)-nets in terms of objects he called generalized orthogonal arrays. Independently, and at about the same time, Schmid defined ordered orthogonal arrays in his 1995 thesis [15] and proved that (T, M, S)-nets can be characterized as (equivalent to) a subclass of these objects. Not sur- prisingly, generalized orthogonal arrays and ordered orthogonal arrays are closely related.
    [Show full text]
  • Cost Justifying Usability a Case-Study at Ericsson
    Master thesis Computer science Thesis ID:MCS-2011-01 January 2011 Cost justifying usability a case-study at Ericsson Parisa Yousefi Pegah Yousefi School of Computing Blekinge Institute of Technology SE 371 39 Karlskrona Sweden 1 This thesis is submitted to the School of Computing at Blekinge Institute of Technology in partial fulfilment of the requirements for the degree of Master of Science in Computer Sci- ence. The thesis is equivalent to 20 weeks of full time studies. Contact Information: Author(s): Parisa Yousefi Address: Näktergalsvagen 5, Rödeby, 371 30 E-mail: [email protected] Pegah Yousefi Address: Drottninggatan 36, Karlskrona, 371 37 E-mail: [email protected] External advisor(s): Helen Sjelvgren Ericsson Address: Ölandsgatan 6 Karlskrona, 371 23 Phone: +46 10 7158732 University advisor(s): Dr Kari Rönkkö, Ph.D Jeff Winter Licentiate School of Computing Internet : www.bth.se/com Blekinge Institute of Technology Phone : +46 455 38 50 00 SE – 371 39 Karlskrona Fax : +46 455 38 50 57 Sweden 2 Abstract Usability in Human-computer Interaction and user- centered design is indeed a key factor, in success of any software products. However, software industries, in particular the attitude of revenue management, express a need of a economic and academic case, for justification of usability discipline. In this study we investigate the level of usability and usability issues and the gaps concerning usability activities and the potential users, in a part of charging system products in Ericsson. Also we try identifying the cost-benefit factors, usability brings to this project, in order to attempt 'justifying the cost of usability for this particular product'.
    [Show full text]